
"""simple docstring"""


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       return max(metric_fn(UpperCAmelCase_		,   UpperCAmelCase_					) for gt in ground_truths					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[]

       if args.gold_data_mode == "qa":
              UpperCamelCase       			=		pd.read_csv(UpperCAmelCase_		,   sep="\t"		,   header=UpperCAmelCase_					)
              for answer_list in data[1]:
                     UpperCamelCase       			=		ast.literal_eval(UpperCAmelCase_					)
                     answers.append(UpperCAmelCase_					)
       else:
              UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
              UpperCamelCase       			=		[[reference] for reference in references]

       UpperCamelCase       			=		UpperCamelCase       			=		UpperCamelCase       			=		0
       for prediction, ground_truths in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              total += 1
              em += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
              fa += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		100.0 * em / total
       UpperCamelCase       			=		100.0 * fa / total

       logger.info(F"F1: {fa:.2f}"					)
       logger.info(F"EM: {em:.2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		args.k
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]

       UpperCamelCase       			=		UpperCamelCase       			=		0
       for hypo, reference in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		set(hypo.split("\t"					)[:k]					)
              UpperCamelCase       			=		set(reference.split("\t"					)					)
              total += 1
              em += len(hypo_provenance & ref_provenance					) / k

       UpperCamelCase       			=		100.0 * em / total
       logger.info(F"Precision@{k}: {em: .2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       def strip_title(UpperCAmelCase_					):
              if title.startswith("\""					):
                     UpperCamelCase       			=		title[1:]
              if title.endswith("\""					):
                     UpperCamelCase       			=		title[:-1]
              return title

       UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_		,   )["input_ids"].to(args.device					)

       UpperCamelCase       			=		rag_model.rag.question_encoder(UpperCAmelCase_					)
       UpperCamelCase       			=		question_enc_outputs[0]

       UpperCamelCase       			=		rag_model.retriever(
           UpperCAmelCase_		,   question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()		,   prefix=rag_model.rag.generator.config.prefix		,   n_docs=rag_model.config.n_docs		,   return_tensors="pt"		,   )
       UpperCamelCase       			=		rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       UpperCamelCase       			=		[]
       for docs in all_docs:
              UpperCamelCase       			=		[strip_title(UpperCAmelCase_					) for title in docs["title"]]
              provenance_strings.append("\t".join(UpperCAmelCase_					)					)
       return provenance_strings



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       with torch.no_grad():
              UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_					)

              UpperCamelCase       			=		inputs_dict.input_ids.to(args.device					)
              UpperCamelCase       			=		inputs_dict.attention_mask.to(args.device					)
              UpperCamelCase       			=		rag_model.generate(  # rag_model overwrites generate
                  UpperCAmelCase_		,   attention_mask=UpperCAmelCase_		,   num_beams=args.num_beams		,   min_length=args.min_length		,   max_length=args.max_length		,   early_stopping=UpperCAmelCase_		,   num_return_sequences=1		,   bad_words_ids=[[0, 0]]		,   )
              UpperCamelCase       			=		rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_		,   skip_special_tokens=UpperCAmelCase_					)

              if args.print_predictions:
                     for q, a in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
                            logger.info("Q: {} - A: {}".format(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              return answers



def 						lowerCamelCase__      (				)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"		,   choices=["rag_sequence", "rag_token", "bart"]		,   type=UpperCAmelCase_		,   help=(
               "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
               " model_name_or_path"
           )		,   )
       parser.add_argument(
           "--index_name"		,   default=UpperCAmelCase_		,   choices=["exact", "compressed", "legacy"]		,   type=UpperCAmelCase_		,   help="RAG model retriever type"		,   )
       parser.add_argument(
           "--index_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   help="Path to the retrieval index"		,   )
       parser.add_argument("--n_docs"		,   default=5		,   type=UpperCAmelCase_		,   help="Number of retrieved docs"					)
       parser.add_argument(
           "--model_name_or_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to pretrained checkpoints or model identifier from huggingface.co/models"		,   )
       parser.add_argument(
           "--eval_mode"		,   choices=["e2e", "retrieval"]		,   default="e2e"		,   type=UpperCAmelCase_		,   help=(
               "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
               " precision@k."
           )		,   )
       parser.add_argument("--k"		,   default=1		,   type=UpperCAmelCase_		,   help="k for the precision@k calculation"					)
       parser.add_argument(
           "--evaluation_set"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a file containing evaluation samples"		,   )
       parser.add_argument(
           "--gold_data_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a tab-separated file with gold samples"		,   )
       parser.add_argument(
           "--gold_data_mode"		,   default="qa"		,   type=UpperCAmelCase_		,   choices=["qa", "ans"]		,   help=(
               "Format of the gold data file"
               "qa - a single line in the following format: question [tab] answer_list"
               "ans - a single line of the gold file contains the expected answer string"
           )		,   )
       parser.add_argument(
           "--predictions_path"		,   type=UpperCAmelCase_		,   default="predictions.txt"		,   help="Name of the predictions file, to be stored in the checkpoints directory"		,   )
       parser.add_argument(
           "--eval_all_checkpoints"		,   action="store_true"		,   help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"		,   )
       parser.add_argument(
           "--eval_batch_size"		,   default=8		,   type=UpperCAmelCase_		,   help="Batch size per GPU/CPU for evaluation."		,   )
       parser.add_argument(
           "--recalculate"		,   help="Recalculate predictions even if the prediction file exists"		,   action="store_true"		,   )
       parser.add_argument(
           "--num_beams"		,   default=4		,   type=UpperCAmelCase_		,   help="Number of beams to be used when generating answers"		,   )
       parser.add_argument("--min_length"		,   default=1		,   type=UpperCAmelCase_		,   help="Min length of the generated answers"					)
       parser.add_argument("--max_length"		,   default=50		,   type=UpperCAmelCase_		,   help="Max length of the generated answers"					)

       parser.add_argument(
           "--print_predictions"		,   action="store_true"		,   help="If True, prints predictions while evaluating."		,   )
       parser.add_argument(
           "--print_docs"		,   action="store_true"		,   help="If True, prints docs retried while generating."		,   )
       UpperCamelCase       			=		parser.parse_args()
       UpperCamelCase       			=		torch.device("cuda" if torch.cuda.is_available() else "cpu"					)
       return args



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       if args.model_type is None:
              UpperCamelCase       			=		infer_model_type(args.model_name_or_path					)
              assert args.model_type is not None
       if args.model_type.startswith("rag"					):
              UpperCamelCase       			=		RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              UpperCamelCase       			=		args.n_docs
              if args.index_name is not None:
                     UpperCamelCase       			=		args.index_name
              if args.index_path is not None:
                     UpperCamelCase       			=		args.index_path
       else:
              UpperCamelCase       			=		BartForConditionalGeneration

       UpperCamelCase       			=		(
           [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info("Evaluate the following checkpoints: %s"		,   UpperCAmelCase_					)

       UpperCamelCase       			=		get_scores if args.eval_mode == "e2e" else get_precision_at_k
       UpperCamelCase       			=		evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path					) and (not args.recalculate):
                     logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path					)					)
                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)
                     continue

              logger.info("***** Running evaluation for {} *****".format(UpperCAmelCase_					)					)
              logger.info("  Batch size = %d"		,   args.eval_batch_size					)
              logger.info("  Predictions will be stored under {}".format(args.predictions_path					)					)

              if args.model_type.startswith("rag"					):
                     UpperCamelCase       			=		RagRetriever.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   retriever=UpperCAmelCase_		,   **UpperCAmelCase_					)
                     model.retriever.init_retrieval()
              else:
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
              model.to(args.device					)

              with open(args.evaluation_set		,   "r"					) as eval_file, open(args.predictions_path		,   "w"					) as preds_file:
                     UpperCamelCase       			=		[]
                     for line in tqdm(UpperCAmelCase_					):
                            questions.append(line.strip()					)
                            if len(UpperCAmelCase_					) == args.eval_batch_size:
                                   UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                                   preds_file.write("\n".join(UpperCAmelCase_					) + "\n"					)
                                   preds_file.flush()
                                   UpperCamelCase       			=		[]
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                            preds_file.write("\n".join(UpperCAmelCase_					)					)
                            preds_file.flush()

                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  get_args()
      main(args)







"""simple docstring"""


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)



def 						lowerCamelCase__      (				)->				Tuple:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."					)
       parser.add_argument(
           "--dataset_name"		,   type=UpperCAmelCase_		,   default="wikitext"		,   help="Name of the training. Explore datasets at: hf.co/datasets."		,   )
       parser.add_argument(
           "--dataset_config"		,   type=UpperCAmelCase_		,   default="wikitext-103-raw-v1"		,   help="Configuration name of the dataset."					)
       parser.add_argument(
           "--tokenizer_name_or_path"		,   type=UpperCAmelCase_		,   default="sayakpaul/unigram-tokenizer-wikitext"		,   help="Tokenizer identifier. Can be a local filepath or a Hub identifier."		,   )
       parser.add_argument(
           "--shard_size"		,   type=UpperCAmelCase_		,   default=10_00		,   help="Number of entries to go in a single shard."		,   )
       parser.add_argument("--split"		,   type=UpperCAmelCase_		,   default="train"		,   choices=["train", "test", "validation"]					)
       parser.add_argument(
           "--limit"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   help="Limit the number of shards (used for debugging)."		,   )
       parser.add_argument(
           "--max_length"		,   type=UpperCAmelCase_		,   default=5_12		,   help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."		,   )
       parser.add_argument(
           "--output_dir"		,   default="tf-tpu"		,   type=UpperCAmelCase_		,   help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."		,   )

       UpperCamelCase       			=		parser.parse_args()
       return args



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       def fn(UpperCAmelCase_					):
              return tokenizer(examples["text"]					)

       return fn



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(tokenized_data["input_ids"]					)					):
              UpperCamelCase       			=		{
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]					)					),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]					)					),
              }
              UpperCamelCase       			=		tf.train.Features(feature=UpperCAmelCase_					)
              UpperCamelCase       			=		tf.train.Example(features=UpperCAmelCase_					)
              UpperCamelCase       			=		example.SerializeToString()
              records.append(UpperCAmelCase_					)
       return records



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		datasets.load_dataset(args.dataset_name		,   args.dataset_config		,   split=args.split					)

       if args.limit is not None:
              UpperCamelCase       			=		min(len(UpperCAmelCase_					)		,   args.limit					)
              UpperCamelCase       			=		dataset.select(range(UpperCAmelCase_					)					)
              print(F"Limiting the dataset to {args.limit} entries."					)

       UpperCamelCase       			=		AutoTokenizer.from_pretrained(args.tokenizer_name_or_path					)

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir					):
                     os.makedirs(args.output_dir					)
              UpperCamelCase       			=		os.path.join(args.output_dir		,   args.split					)
              if not os.path.exists(UpperCAmelCase_					):
                     os.makedirs(UpperCAmelCase_					)
       else:
              UpperCamelCase       			=		os.path.join(args.output_dir		,   args.split					)

       # Tokenize the whole dataset at once.
       UpperCamelCase       			=		tokenize_function(UpperCAmelCase_					)
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=4		,   remove_columns=["text"]					)

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(UpperCAmelCase_					):
              # Concatenate all texts.
              UpperCamelCase       			=		{k: sum(examples[k]		,   []					) for k in examples.keys()}
              UpperCamelCase       			=		len(concatenated_examples[list(examples.keys()					)[0]]					)
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              UpperCamelCase       			=		(total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              UpperCamelCase       			=		{
                  k: [t[i : i + args.max_length] for i in range(0		,   UpperCAmelCase_		,   args.max_length					)]
                  for k, t in concatenated_examples.items()
              }
              return result

       UpperCamelCase       			=		dataset_tokenized.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   batch_size=10_00		,   num_proc=4					)

       UpperCamelCase       			=		0
       UpperCamelCase       			=		0
       for shard in range(0		,   len(UpperCAmelCase_					)		,   args.shard_size					):
              UpperCamelCase       			=		grouped_dataset[shard : shard + args.shard_size]
              UpperCamelCase       			=		len(dataset_snapshot["input_ids"]					)
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   F"dataset-{shard_count}-{records_containing}.tfrecord"					)
              UpperCamelCase       			=		get_serialized_examples(UpperCAmelCase_					)

              with tf.io.TFRecordWriter(UpperCAmelCase_					) as out_file:
                     for i in range(len(UpperCAmelCase_					)					):
                            UpperCamelCase       			=		serialized_examples[i]
                            out_file.write(UpperCAmelCase_					)
                     print("Wrote file {} containing {} records".format(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              shard_count += 1
              total_records += records_containing

       with open(F"split-{args.split}-records-count.txt"		,   "w"					) as f:
              print(F"Total {args.split} records: {total_records}"		,   file=UpperCAmelCase_					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  parse_args()
      main(args)





